Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

2017/03/14 by Junbei Zhang, Zhang, Junbei, Xiaodan Zhu +9 · 4 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1703.04617

openalex publication_date 2017/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.

Citations

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Words or Characters? Fine-grained Gating for Reading Comprehension
• Convolutional Neural Networks for Sentence Classification

Cited by

• Adversarial Examples for Evaluating Reading Comprehension Systems
• Reinforced Mnemonic Reader for Machine Reading Comprehension
• Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks
• Enhancing Sentence Embedding with Generalized Pooling

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